Persuasion, Delegation, and Private Information in Algorithm-Assisted Decisions
This addresses the problem of optimizing human-machine collaboration in decision-making for policymakers and organizations, highlighting potential pitfalls in common practices like human-in-the-loop systems.
The paper tackles the design of algorithm-assisted decision-making systems where a principal must choose between acting on an algorithm's prediction or delegating to an agent with private information, finding that optimal delegation depends on preference alignment and that providing maximal information can reduce decision quality.
A principal designs an algorithm that generates a publicly observable prediction of a binary state. She must decide whether to act directly based on the prediction or to delegate the decision to an agent with private information but potential misalignment. We study the optimal design of the prediction algorithm and the delegation rule in such environments. Three key findings emerge: (1) Delegation is optimal if and only if the principal would make the same binary decision as the agent had she observed the agent's information. (2) Providing the most informative algorithm may be suboptimal even if the principal can act on the algorithm's prediction. Instead, the optimal algorithm may provide more information about one state and restrict information about the other. (3) Well-intentioned policies aiming to provide more information, such as keeping a "human-in-the-loop" or requiring maximal prediction accuracy, could strictly worsen decision quality compared to systems with no human or no algorithmic assistance. These findings predict the underperformance of human-machine collaborations if no measures are taken to mitigate common preference misalignment between algorithms and human decision-makers.